Abstract
Many material properties are manifested in the morphological appearance and characterized with microscopic image, such as scanning electron microscopy (SEM). Polymer compatibility is a key physical quantity of polymer material and commonly and intuitively judged by SEM images. However, human observation and judgement for the images is time-consuming, labor-intensive and hard to be quantified. Computer image recognition with machine learning method can make up the defects of artificial judging, giving accurate and quantitative judgement. We achieve automatic compatibility recognition utilizing convolution neural network and transfer learning method, and the model obtains up to 94% accuracy. We also put forward a quantitative criterion for polymer compatibility with this model. The proposed method can be widely applied to the quantitative characterization of the microstructure and properties of various materials.
Abstract (translated)
许多材料特性体现在其形态结构和用小角度图像进行特征识别等方面,例如扫描电子显微镜(SEM)。聚合物相容性是聚合物材料的关键物理量,通常通过SEM图像直觉地判断。然而,对图像进行人类观察和判断是耗时、劳动密集型且难以量化的。利用机器学习方法计算机图像识别可以弥补人工判断的缺陷,提供准确和定量的判断。我们利用卷积神经网络和迁移学习方法实现自动相容识别,模型准确率达到94%。我们还提出了与该模型的聚合物相容性的定量标准。该方法可以广泛应用于各种材料微观结构和性质的定量表征。
URL
https://arxiv.org/abs/2303.12360
